Another possibility for evaluating the performance of our SSL method is to change the number of unlabeled data during the training. However, using the CIFAR-10 and SVHN
datasets in isolation puts an upper limit on the amount of available unlabeled data. Fortunately, in contrast to CIFAR-10, SVHN has been distributed with the “SVHN-extra” dataset, which includes 531,131 additional digit images and has also been previously used as unlabeled data for evaluation of different SSL methods in [1]}. These additional data come from the same distribution as SVHN does, which allows us to use them in our SSL framework. Fig. REF  shows the trend of test error for our SSL algorithm on SVHN
with 1,000 labels and changing amounts of unlabeled
images from SVHN-extra dataset. The results shows that, increasing the amount of unlabeled data improves the performance of our SSL method, but this improvement is not significant when we provide 40k unlabeled data.
